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Narayanan KK, Weigle AT, Xu L, Mi X, Zhang C, Chen LQ, Procko E, Shukla D. Deep mutational scanning reveals sequence to function constraints for SWEET family transporters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.28.601307. [PMID: 39005363 PMCID: PMC11244857 DOI: 10.1101/2024.06.28.601307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based In silico variant prediction tools have limited utility for engineering plant proteins as they were unable to reproduce our experimental results. We conclude that DMS can produce datasets which, when combined with the right predictive computational frameworks, can direct plant engineering efforts through derivative phenotype selection and evolutionary insights.
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Affiliation(s)
- Krishna K. Narayanan
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Austin T. Weigle
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Lingyun Xu
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Xuenan Mi
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Chen Zhang
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Li-Qing Chen
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Erik Procko
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Cyrus Biotechnology, Inc., Seattle, Washington 98121, United States
| | - Diwakar Shukla
- Department of Chemical & Biomolecular Engineering; Department of Plant Biology; Department of Bioengineering; Department of Chemistry, Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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2
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Taylor AL, Starr TN. Deep mutational scans of XBB.1.5 and BQ.1.1 reveal ongoing epistatic drift during SARS-CoV-2 evolution. PLoS Pathog 2023; 19:e1011901. [PMID: 38157379 PMCID: PMC10783747 DOI: 10.1371/journal.ppat.1011901] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/11/2024] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
Substitutions that fix between SARS-CoV-2 variants can transform the mutational landscape of future evolution via epistasis. For example, large epistatic shifts in mutational effects caused by N501Y underlied the original emergence of Omicron, but whether such epistatic saltations continue to define ongoing SARS-CoV-2 evolution remains unclear. We conducted deep mutational scans to measure the impacts of all single amino acid mutations and single-codon deletions in the spike receptor-binding domain (RBD) on ACE2-binding affinity and protein expression in the recent Omicron BQ.1.1 and XBB.1.5 variants, and we compared mutational patterns to earlier viral strains that we have previously profiled. As with previous deep mutational scans, we find many mutations that are tolerated or even enhance binding to ACE2 receptor. The tolerance of sites to single-codon deletion largely conforms with tolerance to amino acid mutation. Though deletions in the RBD have not yet been seen in dominant lineages, we observe tolerated deletions including at positions that exhibit indel variation across broader sarbecovirus evolution and in emerging SARS-CoV-2 variants of interest, most notably the well-tolerated Δ483 deletion in BA.2.86. The substitutions that distinguish recent viral variants have not induced as dramatic of epistatic perturbations as N501Y, but we identify ongoing epistatic drift in SARS-CoV-2 variants, including interaction between R493Q reversions and mutations at positions 453, 455, and 456, including F456L that defines the XBB.1.5-derived EG.5 lineage. Our results highlight ongoing drift in the effects of mutations due to epistasis, which may continue to direct SARS-CoV-2 evolution into new regions of sequence space.
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Affiliation(s)
- Ashley L. Taylor
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Tyler N. Starr
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
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3
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Wang Y, Zhao Y, Li Y, Zhang K, Fan Y, Li B, Su W, Li S. piggyBac-mediated genomic integration of linear dsDNA-based library for deep mutational scanning in mammalian cells. Cell Mol Life Sci 2023; 80:321. [PMID: 37815732 PMCID: PMC11071730 DOI: 10.1007/s00018-023-04976-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023]
Abstract
Deep mutational scanning (DMS) makes it possible to perform massively parallel quantification of the relationship between genetic variants and phenotypes of interest. However, the difficulties in introducing large variant libraries into mammalian cells greatly hinder DMS under physiological states. Here, we developed two novel strategies for DMS library construction in mammalian cells, namely 'piggyBac-in vitro ligation' and 'piggyBac-in vitro ligation-PCR'. For the first strategy, we took the 'in vitro ligation' approach to prepare high-diversity linear dsDNAs, and integrate them into the mammalian genome with a piggyBac transposon system. For the second strategy, we further added a PCR step using the in vitro ligation dsDNAs as templates, for the construction of high-content genome-integrated libraries via large-scale transfection. Both strategies could successfully establish genome-integrated EGFP-chromophore-randomized libraries in HEK293T cells and enrich the green fluorescence-chromophore amino-acid sequences. And we further identified a novel transcriptional activator peptide with the 'piggyBac-in vitro ligation-PCR' strategy. Our novel strategies greatly facilitate the construction of large variant DMS library in mammalian cells, and may have great application potential in the future.
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Affiliation(s)
- Yi Wang
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Yanjie Zhao
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Yifan Li
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Kaili Zhang
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Yan Fan
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Bo Li
- Beijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Life Science, Beijing Institute of Technology, Beijing, China
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Weijun Su
- School of Medicine, Nankai University, Tianjin, 300071, China.
| | - Shuai Li
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
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4
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Taylor AL, Starr TN. Deep mutational scans of XBB.1.5 and BQ.1.1 reveal ongoing epistatic drift during SARS-CoV-2 evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557279. [PMID: 37745441 PMCID: PMC10515859 DOI: 10.1101/2023.09.11.557279] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Substitutions that fix between SARS-CoV-2 variants can transform the mutational landscape of future evolution via epistasis. For example, large epistatic shifts in mutational effects caused by N501Y underlied the original emergence of Omicron variants, but whether such large epistatic saltations continue to define ongoing SARS-CoV-2 evolution remains unclear. We conducted deep mutational scans to measure the impacts of all single amino acid mutations and single-codon deletions in the spike receptor-binding domain (RBD) on ACE2-binding affinity and protein expression in the recent Omicron BQ.1.1 and XBB.1.5 variants, and we compared mutational patterns to earlier viral strains that we have previously profiled. As with previous RBD deep mutational scans, we find many mutations that are tolerated or even enhance binding to ACE2 receptor. The tolerance of sites to single-codon deletion largely conforms with tolerance to amino acid mutation. Though deletions in the RBD have not yet been seen in dominant lineages, we observe many tolerated deletions including at positions that exhibit indel variation across broader sarbecovirus evolution and in emerging SARS-CoV-2 variants of interest, most notably the well-tolerated Δ483 deletion in BA.2.86. The substitutions that distinguish recent viral variants have not induced as dramatic of epistatic perturbations as N501Y, but we identify ongoing epistatic drift in SARS-CoV-2 variants, including interaction between R493Q reversions and mutations at positions 453, 455, and 456, including mutations like F456L that define the newly emerging EG.5 lineage. Our results highlight ongoing drift in the effects of mutations due to epistasis, which may continue to direct SARS-CoV-2 evolution into new regions of sequence space.
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Affiliation(s)
- Ashley L. Taylor
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Tyler N. Starr
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
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5
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Wei H, Li X. Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes. Front Genet 2023; 14:1087267. [PMID: 36713072 PMCID: PMC9878224 DOI: 10.3389/fgene.2023.1087267] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023] Open
Abstract
Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.
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Affiliation(s)
- Huijin Wei
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China
| | - Xianghua Li
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China
- Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
- The Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China
- Biomedical and Health Translational Centre of Zhejiang Province, Haining, Zhejiang, China
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6
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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7
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Ahmed WS, Philip AM, Biswas KH. Decreased Interfacial Dynamics Caused by the N501Y Mutation in the SARS-CoV-2 S1 Spike:ACE2 Complex. Front Mol Biosci 2022; 9:846996. [PMID: 35936792 PMCID: PMC9355283 DOI: 10.3389/fmolb.2022.846996] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/28/2022] [Indexed: 12/24/2022] Open
Abstract
Coronavirus Disease of 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in a massive health crisis across the globe, with some genetic variants gaining enhanced infectivity and competitive fitness, and thus significantly aggravating the global health concern. In this regard, the recent SARS-CoV-2 alpha, beta, and gamma variants (B.1.1.7, B.1.351, and P.1 lineages, respectively) are of great significance in that they contain several mutations that increase their transmission rates as evident from clinical reports. By the end of March 2021, these variants were accounting for about two-thirds of SARS-CoV-2 variants circulating worldwide. Specifically, the N501Y mutation in the S1 spike receptor binding domain (S1-RBD) of these variants have been reported to increase its affinity for ACE2, although the basis for this is not entirely clear yet. Here, we dissect the mechanism underlying the increased binding affinity of the N501Y mutant for ACE2 using molecular dynamics (MD) simulations of the available ACE2-S1-RBD complex structure (6M0J) and show a prolonged and stable interfacial interaction of the N501Y mutant S1-RBD with ACE2 compared to the wild type S1-RBD. Additionally, we find that the N501Y mutant S1-RBD displays altered dynamics that likely aids in its enhanced interaction with ACE2. By elucidating a mechanistic basis for the increased affinity of the N501Y mutant S1-RBD for ACE2, we believe that the results presented here will aid in developing therapeutic strategies against SARS-CoV-2 including designing of therapeutic agents targeting the ACE2-S1-RBD interaction.
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Affiliation(s)
- Wesam S. Ahmed
- Division of Biological and Biomedical Sciences, College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Angelin M. Philip
- Division of Genomics and Translational Biomedicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Kabir H. Biswas
- Division of Biological and Biomedical Sciences, College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Horne J, Shukla D. Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering. Ind Eng Chem Res 2022; 61:6235-6245. [PMID: 36051311 PMCID: PMC9432854 DOI: 10.1021/acs.iecr.1c04943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Proteins are Nature's molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the pharmaceutical, energy, and biocatalysis fields, among others-where the aim is to ultimately create a protein given desired structural and functional properties. It is often critical to model the relationship between a protein's sequence, folded structure, and biological function to assist in such protein engineering pursuits. However, significant challenges remain in concretely mapping an amino acid sequence to specific protein properties and biological activities. Mutations may enhance or diminish molecular protein function, and the epistatic interactions between mutations result in an inherently complex mapping between genetic modifications and protein function. Therefore, estimating the quantitative effects of mutations on protein function(s) remains a grand challenge of biology, bioinformatics, and many related fields and would rapidly accelerate protein engineering tasks when successful. Such estimation is often known as variant effect prediction (VEP). However, progress has been demonstrated in recent years with the development of machine learning (ML) methods in modeling the relationship between mutations and protein function. In this Review, recent advances in variant effect prediction (VEP) are discussed as tools for protein engineering, focusing on techniques incorporating gains from the broader ML community and challenges in estimating biomolecular functional differences. Primary developments highlighted include convolutional neural networks, graph neural networks, and natural language embeddings for protein sequences.
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Affiliation(s)
- Jesse Horne
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Champaign, Illinois 61801, United States
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering and Department of Bioengineering, University of Illinois Urbana-Champaign, Champaign, Illinois 61801, United States; Department of Plant Biology, Cancer Center at Illinois, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Champaign, Illinois 61801, United States
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9
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Abstract
Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.
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Affiliation(s)
- Jeannette L Tenthorey
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; , ,
| | - Michael Emerman
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; , , .,Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Harmit S Malik
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; , , .,Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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10
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Yépez Y, Marcano-Ruiz M, Bezerra RS, Fam B, Ximenez JPB, Silva WA, Bortolini MC. Evolutionary history of the SARS-CoV-2 Gamma variant of concern (P.1): a perfect storm. Genet Mol Biol 2022; 45:e20210309. [PMID: 35266951 PMCID: PMC8908351 DOI: 10.1590/1678-4685-gmb-2021-0309] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/29/2021] [Indexed: 12/11/2022] Open
Abstract
Our goal was to describe in more detail the evolutionary history of Gamma and two derived lineages (P.1.1 and P.1.2), which are part of the arms race that SARS-CoV-2 wages with its host. A total of 4,977 sequences of the Gamma strain of SARS-CoV-2 from Brazil were analyzed. We detected 194 sites under positive selection in 12 genes/ORFs: Spike, N, M, E, ORF1a, ORF1b, ORF3, ORF6, ORF7a, ORF7b, ORF8, and ORF10. Some diagnostic sites for Gamma lacked a signature of positive selection in our study, but these were not fixed, apparently escaping the action of purifying selection. Our network analyses revealed branches leading to expanding haplotypes with sites under selection only detected when P.1.1 and P.1.2 were considered. The P.1.2 exclusive haplotype H_5 originated from a non-synonymous mutational step (H3509Y) in H_1 of ORF1a. The selected allele, 3509Y, represents an adaptive novelty involving ORF1a of P.1. Finally, we discuss how phenomena such as epistasis and antagonistic pleiotropy could limit the emergence of new alleles (and combinations thereof) in SARS-COV-2 lineages, maintaining infectivity in humans, while providing rapid response capabilities to face the arms race triggered by host immuneresponses.
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Affiliation(s)
- Yuri Yépez
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
| | - Mariana Marcano-Ruiz
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
| | - Rafael S Bezerra
- Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto,
Departamento de Genética, Ribeirão Preto, SP, Brazil
| | - Bibiana Fam
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
| | - João PB Ximenez
- Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto,
Departamento de Genética, Ribeirão Preto, SP, Brazil
| | - Wilson A Silva
- Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto,
Departamento de Genética, Ribeirão Preto, SP, Brazil
- Instituto de Pesquisa do Câncer de Guarapuava, Guarapuava, PR,
Brazil
| | - Maria Cátira Bortolini
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
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11
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Wang Y, Lei R, Nourmohammad A, Wu NC. Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing. eLife 2021; 10:e72516. [PMID: 34878407 PMCID: PMC8683081 DOI: 10.7554/elife.72516] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/07/2021] [Indexed: 11/13/2022] Open
Abstract
As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.
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Affiliation(s)
- Yiquan Wang
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
| | - Ruipeng Lei
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
| | - Armita Nourmohammad
- Department of Physics, University of WashingtonSeattleUnited States
- Max Planck Institute for Dynamics and Self-OrganizationGöttingenGermany
- Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Carle Illinois College of Medicine, University of Illinois at Urbana-ChampaignUrbanaUnited States
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12
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Li SX, Yu F, Chen HX, Zhang XD, Meng LH, Hao K, Zhao Z. Characterization of Ictalurid herpesvirus 1 Glycoprotein ORF59 and Its Potential Role on Virus Entry into the Host Cells. Viruses 2021; 13:v13122393. [PMID: 34960662 PMCID: PMC8709185 DOI: 10.3390/v13122393] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/18/2021] [Accepted: 11/27/2021] [Indexed: 12/19/2022] Open
Abstract
The channel catfish virus (CCV, Ictalurid herpesvirus 1) has caused sustained economic losses in the fish industry because of its strong infectivity and pathogenicity. Thus, it is necessary to determine the function of viral proteins in the CCV infection process. The present study aimed to characterize CCV glycoprotein ORF59 and explore its impact on virus infection in host cells. Firstly, its exclusive presence in the membrane fraction of the cell lysate and subcellular localization verified that CCV ORF59 is a viral membrane protein expressed at late-stage infection. A protein blocking assay using purified His6 tagged ORF59, expressed in sf9 insect cells using a baculovirus expression system, indicated a dose-dependent inhibitory effect of recombinant ORF59 protein on virus invasion. Knockdown of the ORF59 using a short hairpin (shRNA) showed that ORF59 silencing decreased the production of infectious virus particles in channel catfish ovary cells. The results of this study suggest that recombinant ORF59 protein might inhibit CCV entry into the host cells. These findings will promote future studies of the key functions of glycoprotein ORF59 during CCV infection.
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Affiliation(s)
- Shu-Xin Li
- Department of Marine Biology, College of Oceanography, Hohai University, Nanjing 210098, China; (S.-X.L.); (F.Y.); (H.-X.C.); (X.-D.Z.); (L.-H.M.); (K.H.)
| | - Fei Yu
- Department of Marine Biology, College of Oceanography, Hohai University, Nanjing 210098, China; (S.-X.L.); (F.Y.); (H.-X.C.); (X.-D.Z.); (L.-H.M.); (K.H.)
| | - Hong-Xun Chen
- Department of Marine Biology, College of Oceanography, Hohai University, Nanjing 210098, China; (S.-X.L.); (F.Y.); (H.-X.C.); (X.-D.Z.); (L.-H.M.); (K.H.)
| | - Xiao-Dong Zhang
- Department of Marine Biology, College of Oceanography, Hohai University, Nanjing 210098, China; (S.-X.L.); (F.Y.); (H.-X.C.); (X.-D.Z.); (L.-H.M.); (K.H.)
| | - Li-Hui Meng
- Department of Marine Biology, College of Oceanography, Hohai University, Nanjing 210098, China; (S.-X.L.); (F.Y.); (H.-X.C.); (X.-D.Z.); (L.-H.M.); (K.H.)
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Kai Hao
- Department of Marine Biology, College of Oceanography, Hohai University, Nanjing 210098, China; (S.-X.L.); (F.Y.); (H.-X.C.); (X.-D.Z.); (L.-H.M.); (K.H.)
| | - Zhe Zhao
- Department of Marine Biology, College of Oceanography, Hohai University, Nanjing 210098, China; (S.-X.L.); (F.Y.); (H.-X.C.); (X.-D.Z.); (L.-H.M.); (K.H.)
- Correspondence: ; Tel.: +86-025-8378-7653
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